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Record W1974312523 · doi:10.1016/j.bbmt.2007.11.013

Salvage Therapy for Relapsed/Refractory Diffuse Large B Cell Lymphoma

2008· review· en· W1974312523 on OpenAlexaff
Tara Seshadri, John Kuruvilla, Michael Crump, Armand Keating

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2008
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineChemoimmunotherapySalvage therapyOncologyChemotherapyInternal medicineLymphomaRefractory (planetary science)RegimenTransplantationChemotherapy regimenAggressive lymphomaDiffuse large B-cell lymphomaHematopoietic stem cell transplantationRituximab

Abstract

fetched live from OpenAlex

Diffuse large B cell lymphoma (DLBCL), the most common subtype of aggressive lymphoma, has considerable biologic and clinical heterogeneity. Despite recent therapeutic advances, up to 50% of patients relapse after standard chemoimmunotherapy. The International Prognostic Index (IPI) at relapse is of value in providing prognostic information on response to salvage chemotherapy and outcome after autologous hematopoietic cell transplantation (aHCT). Predictive biologic and gene expression markers, however, remain undefined, and require further clarification from additional molecular studies. To date, the standard of care in the management of relapsed/refractory DLBCL is salvage chemotherapy followed by an aHCT for those with chemotherapy-sensitive disease. Currently, there is no standard salvage chemotherapy regimen, and the use of immunotherapy for relapsed disease requires further evaluation. This review focuses on prognostic markers, current salvage therapies, and discusses the role of novel treatment in the management of relapsed/refractory DLBCL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.294
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2008
Admission routes1
Has abstractyes

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